Applied Machine Learning Engineer - Causal Inference Recommendation

About the Team

Come help us build the world's most reliable on-demand logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop the AI/ML to power DoorDash expand Merchant selection and growing Merchants.

About the Role

We’re looking for a passionate Applied Machine Learning expert to join our team. In this role, you will utilize our robust data and machine learning infrastructure to build recommendation systems and implement new AI solutions to expand restaurant selection and drive their growth. You’ll be conceptualizing, designing, and evaluating A/ML solutions. You will be expected to demonstrate a strong command of production-level machine learning and a passion for collaborating with multi-disciplinary teams to set the strategy and execute to grow our business.

You’re excited about this opportunity because you will…

• Develop production machine learning solutions, which are central intelligence to power multiple teams including Sales Operation, Product, and Marketing.
• Partner with engineering and product leaders to help shape the product roadmap leveraging AI/ML.
• Own the modeling life cycle end-to-end including feature creation, model development and deployment, experimentation, monitoring and explainability, and model maintenance.
• Find new ways to use diverse data sources and modeling techniques, such as NLP, ranking, personalization, image classification, and entity resolution to connect with merchants at the right time and provide an AI-driven world-class merchant experience.

We’re excited about you because you have…

• 1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree developing machine learning models with business impact.
• Expertise in applied ML for Causal Inference and Recommendation Systems - both classical and deep learning based. Additional familiarity with experimentation, computer vision, and LLMs!
• M.S. or PhD in Statistics, Computer Science, Economics, Math, Operations Research, Physics, or other quantitative fields.
• Ability to communicate technical details to non-technical stakeholders.
• Strong machine learning background in Python; experience with Spark, PyTorch or TensorFlow preferred.
• Familiarity with Kotlin/Scala.
• You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down.
• The desire for impact with a growth-minded and collaborative mindset.